Christopher G. Lamoureux

Persistence in Variance, Structural Change, and the GARCH Model

This article examines the persistence of the variance, as measured by the generalized autoregressive conditional heteroskedasticity (GARCH) model, in stock-return data. In particular, we investigate the extent to which persistence in variance may be overstated because of the existence of, and failure to take account of, deterministic structural shifts in the model. Both an analysis of daily stock-return data and a Monte Carlo simulation experiment confirm the hypothesis that GARCH measures of persistence in variance are sensitive to this type of model misspecification.

Endogenous Trading Volume and Momentum in Stock-Return Volatility

This article examines the ability of volume data to shed light on the source of persistence in stock-return volatility. A mixture model, in which a latent common factor restricts the joint density of volume and returns, is used to relax the assumption of exogenous volume used in previous studies. We use a point-in-time signal-extraction procedure to identify this latent process and a calibrated simulation to conduct analysis of the viability of the model to explain important properties of the data. Using daily returns and volume on individual stocks, our procedure cannot accommodate serial dependence in squared returns.

Estimation of Stable-Law Parameters: A Comparative Study

The stable distribution has many desirable properties and is applicable in many areas of scientific pursuit (e.g., the study of stock-return behavior). Despite this, little is known about the properties of the various extant estimation techniques for the parameters of the stable laws. This article compares the iterative regression technique with the latest version of the fractile technique, using both simulated and actual data.

Empirical Analysis of the Yield Curve: The Information in the Data Viewed through the Window of Cox, Ingersoll, and Ross

This paper uses recent advances in Bayesian estimation methods to exploit fully and efficiently the time-series and cross-sectional empirical restrictions of the Cox, Ingersoll, and Ross model of the term structure. We examine the extent to which the cross-sectional data (five different instruments) provide information about the model. We find that the time-series restrictions of the two-factor model are generally consistent with the data. However, the models cross-sectional restrictions are not. We show that adding a third factor produces a significant statistical improvement, but causes the average time-series fit to the yields themselves to deteriorate. Copyright The American Finance Association 2002.

Dividends, taxes, and normative portfolio theory

Abstract This paper presents a normative methodology for selection of optimal portfolios in light of possible differential tax treatments of dividends and capital gains. This methodology is applied to actual market data. It is shown that the selection of optimal portfolios is virtually independent of the investors tax rate. Implications of this result for understanding the relationship between dividends and rates of return are discussed. It is concluded that dividends are not priced as cash flows; instead they do matter as they relate to the paying firm. Further, portfolio dividend yields are highly positively correlated with investor risk aversion.

The Relevance of the Distributional Form of Common Stock Returns to the Construction of Optimal Portfolios

In this paper, we compare the robustness in application of the Gaussian assumption of security return distributions to the robustness of the general stable assumption. Using actual stock return data to simulate the “real world,†a stock market is constructed in which stock returns conform to a Gaussian distribution as well as to a stable Pareto-Levy distribution. Using these two sets of stock returns, efficient frontiers are generated under both assumptions of parametric environments. It is shown that the Gaussian assumption, and its incumbent statistical techniques, is preferable to the general stable assumption.

Microstructure with multiple assets: An experimental investigation into direct and indirect dealer competition

Abstract This paper uses the economic laboratory to isolate the effects of direct and indirect competition on dealer profitability. We compare these two settings: (1) three competing dealers in a single asset (direct competition) with (2) three assets with a monopoly dealer in each (indirect competition). We find that: bid–ask spreads are wider, prices are less responsive to order flow (so there is less price discovery), and per-trade dealer profits are larger in the single-asset setting. Important economic differences between these two settings include a heightened adverse selection problem in the three-asset setting and a public good nature of price discovery in the one-asset setting.

Testing Arbitrage Models Without Adding an Error Model: An Application to Cox, Ingersoll, and Ross

We demonstrate a simple procedure to test arbitrage models without adding an auxiliary error model. Our tests rely on the dynamics of the model to draw inference through out-of-sample forecasting. As an illustration, we estimate the Cox et al. model with a rolling sample to forecast zero-coupon yields from 1994 to 2007. We use these forecasts to test the model as both a candidate return generating process and to assess its efficacy as part of a forecasting method. The model is soundly rejected. Since our empirical design maintains the models stochastic singularity, the affine term structure models poor empirical performance cannot be blamed on an unfortunate choice of an auxiliary error model. Unlike earlier studies, the traditional expectations hypothesis holds in our sample, and the model cannot reproduce this feature of the data.

Empirically Confronting Stochastic Singularity: An Application to the Cox, Ingersoll, and Ross Model

We just-identify a no-arbitrage term structure model in estimation and then test it using both a classical orthogonality restriction test and a test of conditional predictive ability. We treat the error structure as unmodeled heterogeneity so that the model is estimated without errors, and the statistical question is whether using the model to characterize the dynamics and patterns in historical data is either useful or optimal as a forecasting tool. The data we use are from the transparent Greenspan regime at the Fed (1989-2005), and we also use a rolling estimation format so that regime shifts are not a likely cause of the models performance. Substantively we find that the model is not a good forecasting device for short rates which in this period are strongly affected by changes in the target Fed Funds rate. For longer term rates, especially at longer forecast horizons where Fed policy has no effect, the model is more informative.